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Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.